Plug-and-Play Explanations for Diverse AI Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to provide universal explanations for various artificial intelligence models, especially in complex scenarios, and existing frameworks like TensorFlow and PyTorch only explain deep learning models within their specific platforms, lacking general applicability and accuracy.
Innovation Solution
A plug-and-play system comprising a service provider server with explanation modules and a plug-and-play manager that automatically identifies and connects an appropriate explanation module to a user's AI model, allowing customizable explanations through a user terminal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single explanation method is used for all AI models, then the system complexity is reduced, but the explanation accuracy decreases because different AI models have different characteristics
Solution Approach 1:
The explanation system is segmented into multiple specialized explanation modules, each designed to handle specific AI model types (decision trees, deep learning models, Bayesian models). This segmentation allows each module to provide accurate explanations tailored to its specific model type while maintaining overall system manageability through modular architecture.
Solution Approach 2:
The patent creates a universal explanation framework that can handle multiple AI model types through a common interface and selection mechanism. The system provides a unified way to request explanations while automatically selecting the appropriate specialized module, achieving both universality and specificity.
2Measurement precision
If separate explanation algorithms are provided for each AI model type, then the explanation accuracy is improved, but the device complexity increases
Solution Approach 1:
The explanation system is divided into independent, specialized modules for different AI model types. Each module can be developed, tested, and maintained separately, which improves accuracy for each model type while reducing overall system complexity through modular design and independent development.
Solution Approach 2:
An intermediary selection mechanism acts as a mediator between the user's explanation request and the appropriate specialized explanation module. This intermediary component automatically identifies the model type and routes the request to the correct module, simplifying the user interface while maintaining specialized accuracy.
3Measurement precision
If existing deep learning frameworks explain only their own models, then the framework-specific explanation accuracy is improved, but the adaptability decreases
Solution Approach 1:
The patent creates a universal explanation platform that can interface with multiple deep learning frameworks (TensorFlow, PyTorch, etc.) through standardized interfaces. This allows the system to provide accurate explanations for models from different frameworks without requiring separate specialized systems for each framework.
Solution Approach 2:
The system introduces intermediary layers that act as translators between different AI frameworks and the explanation modules. These intermediaries convert framework-specific model representations into a unified internal representation that can be processed by the explanation modules, enabling cross-framework compatibility while maintaining explanation accuracy.
Data Source
Figure 1
Figure 2
Figure 3~4A
AI summary
A method of providing an explanation of an artificial intelligence model based on a plug-and-plug mode includes a service provider server setting a type of an artificial intelligence model, a plug-and-play manager recognizing an artificial intelligence model and creating a list of available explanation modules on the basis of a recognition result when the artificial intelligence model is input, the service provider server providing key information on explanation modules to a user terminal, the user terminal selecting one of the explanation modules and notifying the service provider server of the selected explanation module, the plug-and-play manager deriving the selected explanation module from among the explanation modules in the list, and the derived explanation module providing an explanation of the input artificial intelligence model to the user terminal.